class_docstr: Our own implementation of stacked GRU.
  Needed for the decoder, because we do input feeding.
code: def forward(self, input_feed, hidden):
      h_1 = []
      for i, layer in enumerate(self.layers):
          h_1_i = layer(input_feed, hidden[0][i])
          input_feed = h_1_i
          if i + 1 != self.num_layers:
              input_feed = self.dropout(input_feed)
          h_1 += [h_1_i]
  
      h_1 = torch.stack(h_1)
      return input_feed, (h_1,)
  
file_path: OpenNMT-py/onmt/modules/stacked_rnn.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the StackedGRU obj's underlying member method code with example usage: obj.forward(input_feed, hidden) and returns: input_feed,  h_1